Dual-Tree Complex Wavelet Transform on Row Mean and Column Mean of Images for CBIR
Nst Sai, R. C. Patil · 2011
With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. This paper describes effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Dual Tree Complex Wavelet Transform on row mean and column mean of images for CBIR”. This paper present 6 techniques for calculating feature vector of color image. As Dual-Tree Discrete Wavelet Transform decomposition level goes on increasing then feature vector size goes on decreasing. One of the advantages of the dual-tree complex wavelet transform is that it can be used to implement 2D wavelet transforms that are more selective with respect to orientation than is the separable 2D DWT. Most of the natural images have short span high frequencies and low frequencies extending for larger span. Hence, the design of our feature vector is such a way that it provides higher spatial localization and lower frequency resolution at higher frequencies and the reverse for lower frequencies. All the techniques are tested on database which include 800 images with 8 images classes. Average precision and average recall values calculated by using 40 query images from the database. We have determined the capability of automatic indexing by analyzing image content: texture as features and by applying a similarity measure Euclidean distance.